Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/mawady/awesome-cv

Curated educational list for computer vision
https://github.com/mawady/awesome-cv
awesome awesome-list computer-science computer-vision data-science deep-learning education image-processing machine-learning
Last synced: 3 months ago
JSON representation
Curated educational list for computer vision
Host: GitHub
URL: https://github.com/mawady/awesome-cv
Owner: mawady
Created: 2021-10-27T11:54:09.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2023-11-01T16:33:33.000Z (about 1 year ago)
Last Synced: 2024-05-22T23:07:33.308Z (5 months ago)
Topics: awesome, awesome-list, computer-science, computer-vision, data-science, deep-learning, education, image-processing, machine-learning
Homepage:
Size: 72.3 KB
Stars: 103
Watchers: 4
Forks: 23
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

ultimate-awesome - awesome-cv - Curated educational list for computer vision. (Other Lists / PowerShell Lists)
README

        # Curated educational list for computer vision

> * **[Reference Books](#reference-books)**

> * **[Online Courses](#online-courses)**

> * **[Uni Courses](#uni-courses)**

> * **[YouTube Playlists](#youtube-playlists)**

> * **[Python Libraries](#python-libraries)**

> * **[MATLAB/Octave Libraries](#matlab-libraries)**

> * **[Repos](#repos)**

> * **[Dataset Collections](#dataset-collections)**

> * **[Task Management Tools](#task-management-tools)**

> * **[Annotation Tools](#annotation-tools)**

> * **[Awesome Lists](#awesome-lists)**

> * **[Misc](#misc)**

> * **[Conferences](#conferences)**

> * **[Journals](#journals)**

> * **[Summer Schools](#summer-schools)**

> * **[YouTube Channels](#youtube-channels)**

> * **[Mailing Lists](#mailing-lists)**

---

## Reference Books

| Book | Links |

| --------------- | --------------- |

| Nixon, Mark, and Alberto Aguado. “Feature extraction and image processing for computer vision.” Academic press, (2019). | [goodreads](https://www.goodreads.com/book/show/14788673-feature-extraction-and-image-processing-for-computer-vision) |

| González, Rafael Corsino and Richard E. Woods. “Digital image processing, 4th Edition.” (2018). | [goodreads](https://www.goodreads.com/book/show/42937189-digital-image-processing) |

| Prince, Simon. “Computer Vision: Models, Learning, and Inference.” (2012). | [goodreads](https://www.goodreads.com/book/show/15792261-computer-vision) |

| Forsyth, David Alexander and Jean Ponce. “Computer Vision - A Modern Approach, Second Edition.” (2011). |[goodreads](https://www.goodreads.com/book/show/14857613-computer-vision) |

| Szeliski, Richard. “Computer Vision - Algorithms and Applications.” Texts in Computer Science (2010). | [goodreads](https://www.goodreads.com/book/show/9494221-computer-vision) | 

| Bishop, Charles M.. “Pattern recognition and machine learning, 5th Edition.” Information science and statistics (2007). | [goodreads](https://www.goodreads.com/book/show/37572203-pattern-recognition-and-machine-learning) |

| Harltey, Andrew and Andrew Zisserman. “Multiple view geometry in computer vision (2. ed.).” (2003). | [goodreads](https://www.goodreads.com/book/show/89897.Multiple_View_Geometry_in_Computer_Vision) |

| Stockman, George C. and Linda G. Shapiro. “Computer Vision.” (2001). | [goodreads](https://www.goodreads.com/book/show/19371156-computer-vision) |

---

## Online Courses

| Course | Tags | Platform |

| --------------- | --------------- | --------------- |

| [Version Control with Git](https://learn.udacity.com/courses/ud123) | `Git` | Udacity |

| [Git Essential Training](https://www.linkedin.com/learning/git-essential-training-19417064/) | `Git` | LinkedIn Learning |

| [Learning GitHub](https://www.linkedin.com/learning/learning-github-18719601/) | `Git` | LinkedIn Learning |

| [Introduction to Python Programming](https://www.udacity.com/course/introduction-to-python--ud1110) | `Programming` | Udacity |

| [Learning Python](https://www.linkedin.com/learning/learning-python) | `Programming` | LinkedIn Learning |

| [Intro to Data Science](https://www.udacity.com/courses/ud359) | `Data Science` | Udacity |

| [Intro to Data Analysis](https://www.udacity.com/courses/ud170) | `Data Science` | Udacity |

| [Python Data Analysis](https://www.linkedin.com/learning/python-data-analysis-2) | `Data Science` | LinkedIn Learning | 

| [Segmentation and Clustering](https://www.udacity.com/course/segmentation-and-clustering--ud981) | `Data Science` | Udacity |

| [Python for Data Science Essential Training Part 1](https://www.linkedin.com/learning/python-for-data-science-essential-training-part-1) | `Data Science` | LinkedIn Learning | 

| [Python for Data Science Essential Training Part 2](https://www.linkedin.com/learning/python-for-data-science-essential-training-part-2) | `Data Science` | LinkedIn Learning | 

| [Introduction to Machine Learning Course](https://www.udacity.com/course/intro-to-machine-learning--ud120) | `Machine Learning` | Udacity |

| [Machine Learning with Scikit-Learn](https://www.linkedin.com/learning/machine-learning-with-scikit-learn) | `Machine Learning` | LinkedIn Learning | 

| [Intro to Deep Learning with PyTorch](https://www.udacity.com/course/deep-learning-pytorch--ud188) | `Deep Learning` | Udacity |

| [Introduction to Computer Vision](https://www.udacity.com/courses/ud810) | `Computer Vision` | Udacity |

| [OpenCV for Python Developers](https://www.linkedin.com/learning/opencv-for-python-developers) | `Computer Vision` | LinkedIn Learning | 

---

## Uni Courses

| Course | Tags | University |

| --------------- | --------------- | --------------- |

| [Introduction to Computer Vision](https://browncsci1430.github.io/webpage/) |`Computer Vision` | Brown |

| [Advances in Computer Vision](http://6.869.csail.mit.edu/sp22/) |`Computer Vision` | MIT |

| [Deep Learning for Computer Vision](http://cs231n.stanford.edu) |`Computer Vision` `Deep Learning`  | Stanford |

---

## YouTube Playlists

| Course | Year | Instructor | University |

| --------------- | --------------- | --------------- | --------------- | 

| [Computer Vision](https://www.youtube.com/playlist?list=PL05umP7R6ij35L2MHGzis8AEHz7mg381_)| 2021 | Andreas Geiger | University of Tübingen |

| [Computer Vision](https://www.youtube.com/playlist?list=PLd3hlSJsX_IkXSinyREhlMjFvpNfpazfN) | 2021 | Yogesh S Rawat / Mubarak Shah | University of Central Florida |

| [Advanced Computer Vision](https://www.youtube.com/playlist?list=PLd3hlSJsX_Ilwca04yxhrjcdzx7BS2vDh) | 2021| Mubarak Shah | University of Central Florida |

| [Deep Learning for Computer Vision](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) | 2020 | Justin Johnson | University of Michigan |

| [Advanced Deep Learning for Computer Vision](https://www.youtube.com/playlist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39)| 2020 | Laura Leal-Taixé / Matthias Niessner  | Technical University of Munich |

| [Introduction to Digital Image Processing](https://www.youtube.com/playlist?list=PL2mBI0yFsKk-p73KQ4iPdsi10hQC4Zd-0)| 2020 | Ahmadreza Baghaie | New York Institute of Technology|

| [Quantitative Imaging](https://www.youtube.com/playlist?list=PLTWuXgjdOrnmXVVQG5DRkVeOIGOcTmCIw) | 2019 | Kevin Mader | ETH Zurich |

| [Convolutional Neural Networks for Visual Recognition](https://www.youtube.com/playlist?list=PLf7L7Kg8_FNxHATtLwDceyh72QQL9pvpQ) | 2017 | Fei-Fei Li | Stanford University  |

| [Introduction to Digital Image Processing](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BUf60vkjePfcOQc8sHxmnDX) | 2015|Rich Radke | Rensselaer Polytechnic Institute|

| [Machine Learning for Robotics and Computer Vision](https://www.youtube.com/playlist?list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl) | 2014| Rudolph Triebel |  Technical University of Munich |

| [Multiple View Geometry](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJn6udZ34tht9EVIW7lbeo4) | 2013 | Daniel Cremers | Technical University of Munich |

| [Variational Methods for Computer Vision](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJ7A2iIH5L5ztqqrWYjP2RI) | 2013 | Daniel Cremers | Technical University of Munich |

| [Computer Vision](https://www.youtube.com/playlist?list=PLd3hlSJsX_ImKP68wfKZJVIPTd8Ie5u-9) | 2012| Mubarak Shah | University of Central Florida |

| [Image and video processing](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A79y1StvUUqgyL-O0fZh2rs) | - | Guillermo Sapiro | Duke University|

---

## Python Libraries

| Library | Description |

| --------------- | --------------- |

| [OpenCV](https://github.com/opencv/opencv) | Open Source Computer Vision Library|

| [Pillow](https://github.com/python-pillow/Pillow)| The friendly PIL fork (Python Imaging Library)|

| [scikit-image](https://github.com/scikit-image/scikit-image) | collection of algorithms for image processing|

| [SciPy](https://github.com/scipy/scipy)| open-source software for mathematics, science, and engineering|

| [mmcv](https://github.com/open-mmlab/mmcv)| OpenMMLab foundational library for computer vision research |

| [imutils](https://github.com/PyImageSearch/imutils) | A series of convenience functions to make basic image processing operations|

| [pgmagick](https://github.com/hhatto/pgmagick)| python based wrapper for GraphicsMagick/ImageMagick|

| [Mahotas](https://github.com/luispedro/mahotas) | library of fast computer vision algorithms (last updated: 2021)|

| [SimpleCV](https://github.com/sightmachine/SimpleCV#raspberry-pi) | The Open Source Framework for Machine Vision (last updated: 2015)|

---

## MATLAB Libraries

| Library | Description |

| --------------- | --------------- |

| [PMT](https://pdollar.github.io/toolbox/) | Piotr's Computer Vision Matlab Toolbox|

| [matlabfns](https://www.peterkovesi.com/matlabfns/)| MATLAB and Octave Functions for Computer Vision and Image Processing, P. Kovesi, University of Western Australia|

| [VLFeat](https://www.vlfeat.org/index.html) | open source library implements popular computer vision algorithms, A. Vedaldi and B. Fulkerson|

| [MLV](https://github.com/bwlabToronto/MLV_toolbox) | Mid-level Vision Toolbox (MLVToolbox), BWLab, University of Toronto|

| [ElencoCode](https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0) | Loris Nanni's CV functions, University of Padova|

---

## Repos

### Tags: Object Classification `[ObjCls]`, Object Detection `[ObjDet]`, Object Segmentation `[ObjSeg]`, General Library `[GenLib]`, Text Reading / Object Character Recognition `[OCR]`, Action Recognition `[ActRec]`, Object Tracking `[ObjTrk]`, Data Augmentation `[DatAug]`, Simultaneous Localization and Mapping `[SLAM]`, Outlier/Anomaly/Novelty Detection `[NvlDet]`, Content-based Image Retrieval `[CBIR]`, Image Enhancement `[ImgEnh]`, Aesthetic Assessment `[AesAss]`, Explainable Artificial Intelligence `[XAI]`, Text-to-Image Generation `[TexImg]`, Pose Estimation `[PosEst]`, Video Matting `[VidMat]`, Eye Tracking `[EyeTrk]`

| Repo | Tags | Description |

| --------------- | --------------- | --------------- |

| [computervision-recipes](https://github.com/microsoft/computervision-recipes) | `[GenLib]` | Microsoft, Best Practices, code samples, and documentation for Computer Vision |

| [FastAI](https://github.com/fastai/fastai) | `[GenLib]` | FastAI, Library over PyTorch used for learning and practicing machine learning and deep learning |

| [pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning) | `[GenLib]` | PyTorchLightning, Lightweight PyTorch wrapper for high-performance AI research |

| [ignite](https://github.com/pytorch/ignite) | `[GenLib]` | PyTorch, High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently |

| [pytorch_geometric](https://github.com/pyg-team/pytorch_geometric) | `[GenLib]` | Graph Neural Network Library for PyTorch |

| [kornia](https://github.com/kornia/kornia) | `[GenLib]` | Open Source Differentiable Computer Vision Library |

| [ncnn](https://github.com/Tencent/ncnn) | `[GenLib]` | Tencent, High-performance neural network inference framework optimized for the mobile platform |

| [MediaPipe](https://github.com/google/mediapipe) | `[ObjDet]` `[ObjSeg]` `[ObjTrk]` `[GenLib]` | Google, iOS - Andriod - C++ - Python - Coral, Face Detection - Face Mesh - Iris - Hands - Pose - Holistic - Hair Segmentation - Object Detection - Box Tracking	- Instant Motion Tracking	- Objectron - KNIFT (Similar to SIFT) |

| [PyTorch image models](https://github.com/rwightman/pytorch-image-models) | `[ObjCls]` | rwightman, PyTorch image classification models, scripts, pretrained weights |

| [mmclassification](https://github.com/open-mmlab/mmclassification) | `[ObjCls]` | OpenMMLab, Image Classification Toolbox and Benchmark |

| [vit-pytorch](https://github.com/lucidrains/vit-pytorch) | `[ObjCls]` | SOTA for vision transformers |

| [face_classification](https://github.com/oarriaga/face_classification) | `[ObjCls]` `[ObjDet]`| Real-time face detection and emotion/gender classification |

| [mmdetection](https://github.com/open-mmlab/mmdetection) | `[ObjDet]` | OpenMMLab, Image Detection Toolbox and Benchmark |

| [detectron2](https://github.com/facebookresearch/detectron2) | `[ObjDet]` `[ObjSeg]` | Facebook, FAIR's next-generation platform for object detection, segmentation and other visual recognition tasks |

| [detr](https://github.com/facebookresearch/detr) | `[ObjDet]` | Facebook, End-to-End Object Detection with Transformers |

| [libfacedetection](https://github.com/ShiqiYu/libfacedetection) | `[ObjDet]` | An open source library for face detection in images, speed: ~1000FPS |

| [FaceDetection-DSFD](https://github.com/Tencent/FaceDetection-DSFD) | `[ObjDet]` | Tencent, SOTA face detector |

| [object-Detection-Metrics](https://github.com/rafaelpadilla/Object-Detection-Metrics) | `[ObjDet]` | Most popular metrics used to evaluate object detection algorithms |

| [SAHI](https://github.com/obss/sahi) | `[ObjDet]` `[ObjSeg]` | A lightweight vision library for performing large scale object detection/ instance segmentation |

| [yolov5](https://github.com/ultralytics/yolov5) | `[ObjDet]` | ultralytics |

| [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet) [pjreddie/darknet](https://github.com/pjreddie/darknet) | `[ObjDet]` | YOLOv4 / Scaled-YOLOv4 / YOLOv3 / YOLOv2 |

| [U-2-Net](https://github.com/xuebinqin/U-2-Net) | `[ObjDet]` | ultralytics U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection |

| [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch) | `[ObjSeg]` | qubvel, PyTorch segmentation models with pretrained backbones |

| [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) | `[ObjSeg]` | OpenMMLab, Semantic Segmentation Toolbox and Benchmark |

| [mmocr](https://github.com/open-mmlab/mmocr) | `[OCR]` | OpenMMLab, Text Detection, Recognition and Understanding Toolbox |

| [pytesseract](https://github.com/madmaze/pytesseract) | `[OCR]` | A Python wrapper for Google Tesseract |

| [EasyOCR](https://github.com/JaidedAI/EasyOCR) | `[OCR]` | Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc |

| [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) | `[OCR]` | Practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices|

| [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg) | `[ObjSeg]` | Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc|

| [mmtracking](https://github.com/open-mmlab/mmtracking) | `[ObjTrk]` | OpenMMLab, Video Perception Toolbox for object detection and tracking |

| [mmaction](https://github.com/open-mmlab/mmaction) | `[ActRec]` | OpenMMLab, An open-source toolbox for action understanding based on PyTorch |

| [albumentations](https://github.com/albumentations-team/albumentations) | `[DatAug]` | Fast image augmentation library and an easy-to-use wrapper around other libraries |

| [ORB_SLAM2](https://github.com/raulmur/ORB_SLAM2) | `[SLAM]` | Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities |

| [pyod](https://github.com/yzhao062/pyod) | `[NvlDet]` | Python Toolbox for Scalable Outlier Detection (Anomaly Detection) |

| [imagededup](https://github.com/idealo/imagededup) | `[CBIR]` | Image retrieval, CBIR, Find duplicate images made easy! |

| [image-match](https://github.com/ProvenanceLabs/image-match) | `[CBIR]` | Image retrieval, CBIR, Quickly search over billions of images |

| [Bringing-Old-Photos-Back-to-Life](https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life) | `[ImgEnh]` | Microsoft, Bringing Old Photo Back to Life (CVPR 2020 oral) |

| [image-quality-assessment](https://github.com/idealo/image-quality-assessment) | `[AesAss]` | Idealo, Image Aesthetic, NIMA model to predict the aesthetic and technical quality of images |

| [aesthetics](https://github.com/ylogx/aesthetics) | `[AesAss]` | Image Aesthetics Toolkit using Fisher Vectors |

| [pytorch-cnn-visualizations](https://github.com/utkuozbulak/pytorch-cnn-visualizations) | `[XAI]` | Pytorch implementation of convolutional neural network visualization techniques |

| [DALLE2-pytorch](https://github.com/lucidrains/DALLE2-pytorch) | `[TexImg]` | Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch |

| [imagen-pytorch](https://github.com/lucidrains/imagen-pytorch) | `[TexImg]` | Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch |

| [openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)| `[PosEst]` | OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation |

| [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting) | `[VidMat]` | Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML! |

| [fastudp](https://github.com/visual-layer/fastdup) | `[NvlDet]` `[CBIR]` | An unsupervised and free tool for image and video dataset analysis |

| [Random-Erasing](https://github.com/zhunzhong07/Random-Erasing) | `[DatAug]` | Random Erasing Data Augmentation in PyTorch |

| [CutMix-PyTorch](https://github.com/clovaai/CutMix-PyTorch) | `[DatAug]` | Official Pytorch implementation of CutMix regularizer |

| [keras-cv](https://github.com/keras-team/keras-cv) | `[GenLib]` | Library of modular computer vision oriented Keras components |

| [PsychoPy](https://github.com/psychopy/psychopy) | `[EyeTrk]` | Library for running psychology and neuroscience experiments |

---

## Dataset Collections

- [PyTorch - CV Datasets](https://pytorch.org/vision/stable/datasets.html), Meta

- [Tensorflow - CV Datasets](https://www.tensorflow.org/datasets/catalog/overview#image), Google

- [CVonline: Image Databases](https://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm), Edinburgh University, Thanks to Robert Fisher!

- [Yet Another Computer Vision Index To Datasets (YACVID)](http://yacvid.hayko.at), Thanks to Hayko Riemenschneider!

- [Kaggle](https://www.kaggle.com/datasets?tags=13207-Computer+Vision)

- [PaperWithCode](https://paperswithcode.com/area/computer-vision), Meta

- [RoboFlow](https://public.roboflow.com)

- [VisualData](https://visualdata.io/discovery)

- [CUHK Computer Vision](http://www.ee.cuhk.edu.hk/~xgwang/datasets.html)

- [VGG - University of Oxford](https://www.robots.ox.ac.uk/~vgg/data/)

---

## Task Management Tools

- [MLflow](https://mlflow.org), Platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry

- [Apache Airflow](https://airflow.apache.org), Apache/AirBnB, Platform created by the community to programmatically author, schedule and monitor workflows

- [Ploomber](https://github.com/ploomber/ploomber), fastest way to build data pipelines.

---

## Annotation Tools

- [VoTT](https://github.com/microsoft/VoTT), Microsoft, Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos

- [labelme](https://github.com/wkentaro/labelme), Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation)

- [labelImg](https://github.com/tzutalin/labelImg), Graphical image annotation tool and label object bounding boxes in images

- [VIA](https://www.robots.ox.ac.uk/~vgg/software/via/), VGG Oxford, HTML-based standalone manual annotation software for image, audio and video

- [FiftyOne](https://github.com/voxel51/fiftyone), open-source tool for building high-quality datasets and computer vision models

---

## Awesome Lists

- [anomaly-detection-resources](https://github.com/yzhao062/anomaly-detection-resources), Anomaly detection related books, papers, videos, and toolboxes

- [awesome-satellite-imagery-datasets](https://github.com/chrieke/awesome-satellite-imagery-datasets) List of satellite image training datasets with annotations for computer vision and deep learning

- [awesome-Face_Recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition), Computer vision papers about faces.

- [the-incredible-pytorch](https://github.com/ritchieng/the-incredible-pytorch), Curated list of tutorials, papers, projects, communities and more relating to PyTorch

---

## Misc

- How to build a good poster - [[Link1](https://urc.ucdavis.edu/sites/g/files/dgvnsk3561/files/local_resources/documents/pdf_documents/How_To_Make_an_Effective_Poster2.pdf)] [[Link2](https://www.animateyour.science/post/How-to-design-an-award-winning-conference-poster)] [[Link3](https://www.jamiebgall.co.uk/post/powerful-posters)]

- How to report a good report - [[Link1](https://cs.swan.ac.uk/~csbob/teaching/cs354-projectSpec/laramee10projectGuideline.pdf)] [[link2](https://www.cst.cam.ac.uk/teaching/part-ii/projects/dissertation)]

- [The "Python Machine Learning (3rd edition)" book code repository](https://github.com/rasbt/python-machine-learning-book-3rd-edition)

- [Multithreading with OpenCV-Python to improve video processing performance](https://nrsyed.com/2018/07/05/multithreading-with-opencv-python-to-improve-video-processing-performance/)

- [Computer Vision Zone](https://www.computervision.zone/) - Videos and implementations for computer vision projects

- [MadeWithML](https://github.com/GokuMohandas/MadeWithML), Learn how to responsibly deliver value with ML

- [d2l-en](https://github.com/d2l-ai/d2l-en), Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 200 universities

- [Writing Pet Peeves](https://www.cs.ubc.ca/~tmm/writing.htmt), writing guide for correctness, references, and style

- [Hitchhiker's Guide to Python](https://docs.python-guide.org), Python best practices guidebook, written for humans

- [python-fire](https://github.com/google/python-fire), Google, a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.

- [shotcut](https://shotcut.org), a free, open source, cross-platform video editor.

- [PyTorch Computer Vision Cookbook](https://github.com/PacktPublishing/PyTorch-Computer-Vision-Cookbook), PyTorch Computer Vision Cookbook, Published by Packt.

- [Machine Learning Mastery - Blogs](https://machinelearningmastery.com/blog/), Blogs written by [Jason Brownlee](https://scholar.google.com/citations?hl=en&user=hVaJhRYAAAAJ) about machine learning.

- [PyImageSearch - Blogs](https://pyimagesearch.com/blog/), Blogs written by [Adrian Rosebrock](https://scholar.google.com/citations?user=bLEhONMAAAAJ&hl) about computer vision.

- [jetson-inference](https://github.com/dusty-nv/jetson-inference), guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

---

## Conferences

- CORE Rank A: 

  - ICCV: International Conference on Computer Vision (IEEE) 

  - CVPR: Conference on Computer Vision and Pattern Recognition (IEEE) 

  - ECCV: European Conference on Computer Vision (Springer) 

  - WACV: Winter Conference/Workshop on Applications of Computer Vision (IEEE) 

  - ICASSP: International Conference on Acoustics, Speech, and Signal Processing (IEEE) 

  - MICCAI: Conference on Medical Image Computing and Computer Assisted Intervention (Springer) 

  - IROS: International Conference on Intelligent Robots and Systems (IEEE) 

  - ACMMM: ACM International Conference on Multimedia (ACM) 

- CORE Rank B 

  - ACCV: Asian Conference on Computer Vision (Springer) 

  - VCIP: International Conference on Visual Communications and Image Processing (IEEE) 

  - ICIP: International Conference on Image Processing (IEEE) 

  - CAIP: International Conference on Computer Analysis of Images and Patterns (Springer) 

  - VISAPP: International Conference on Vision Theory and Applications (SCITEPRESS) 

  - ICPR: International Conference on Pattern Recognition (IEEE) 

  - ACIVS: Conference on Advanced Concepts for Intelligent Vision Systems (Springer) 

  - EUSIPCO: European Signal Processing Conference (IEEE) 

  - ICRA: International Conference on Robotics and Automation (IEEE) 

  - BMVC: British Machine Vision Conference (organized by BMVA: British Machine Vision Association and Society for Pattern Recognition) 

- CORE Rank C: 

  - ICISP: International Conference on Image and Signal Processing (Springer) 

  - ICIAR: International Conference on Image Analysis and Recognition (Springer) 

  - ICVS: International Conference on Computer Vision Systems (Springer) 

- Unranked but popular 

  - MIUA: Conference on Medical Image Understanding and Analysis (organized by BMVA: British Machine Vision Association and Society for Pattern Recognition)

  - EUVIP: European Workshop on Visual Information Processing (IEEE, organized by EURASIP: European Association for Signal Processing) 

  - CIC: Color and Imaging Conference (organized by IS&T: Society for Imaging Science and Technology) 

  - CVCS: Colour and Visual Computing Symposium

  - DSP: International Conference on Digital Signal Processing

---

## Journals

- Tier 1

  - IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)

  - IEEE Transactions on Image Processing (IEEE TIP)

  - IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT)

  - Springer International Journal of Computer Vision (Springer IJCV)

  - Elsevier Pattern Recognition (Elsevier PR)

  - Elsevier Computer Vision and Image Understanding (Elsevier CVIU)

  - Elsevier Expert Systems with Applications

  - Elsevier Neurocomputing, Springer Neural Computing and Applications

- Tier 2

  - Elsevier Image and Vision Computing (Elsevier IVC)

  - Elsevier Pattern Recognition Letters (Elsevier PR Letters)

  - Elsevier Journal of Visual Communication and Image Representation

  - Springer Journal of Mathematical Imaging and Vision

  - SPIE Journal of Electronic Imaging

  - IET Image Processing

  - Springer Pattern Analysis and Applications (Springer PAA)

  - Springer Machine Vision and Applications (Springer MVA)

  - IET Computer Vision

- Open Access

  - IEEE Access

  - MDPI Journal of Imaging

---

## Summer Schools

- International Computer Vision Summer School (IVCSS) [2007-Present], Sicily, Italy [[2023](https://iplab.dmi.unict.it/icvss2023/)]

- Machine Intelligence and Visual Computing Summer School (VISUM) [2013-2022], Porto, Portugal [[2022](https://visum.inesctec.pt)]

- BMVA British Computer Vision Summer School (CVSS) [2013-2020,2023], UK [[Website](https://britishmachinevisionassociation.github.io/summer-school)]

---

## YouTube Channels

- [@AurelienGeron](https://www.youtube.com/@AurelienGeron) `[Individual]`, Aurélien Géron: former lead of YouTube's video classification team, and author of the O'Reilly book Hands-On Machine Learning with Scikit-Learn and TensorFlow.

- [@howardjeremyp](https://www.youtube.com/@howardjeremyp) `[Individual]`, Jeremy Howard: former president and chief scientist of Kaggle, and co-founder of fast.ai.

- [@PieterAbbeel](https://www.youtube.com/@PieterAbbeel) `[Individual]`, Pieter Abbeel: professor of electrical engineering and computer sciences, University of California, Berkeley.

- [@pascalpoupart3507](https://www.youtube.com/@pascalpoupart3507) `[Individual]`, Pascal Poupart: professor in the David R. Cheriton School of Computer Science at the University of Waterloo.

- [@MatthiasNiessner](https://www.youtube.com/@MatthiasNiessner) `[Individual]`, Matthias Niessner: Professor at the Technical University of Munich and head of the Visual Computing Lab.

- [@MichaelBronsteinGDL](https://www.youtube.com/@MichaelBronsteinGDL) `[Individual]`, Michael Bronstein: DeepMind Professor of AI, University of Oxford / Head of Graph Learning Research, Twitter.

- [@DeepFindr](https://www.youtube.com/@DeepFindr) `[Individual]`, Videos about all kinds of Machine Learning / Data Science topics.

- [@deeplizard](https://www.youtube.com/@deeplizard) `[Individual]`, Videos about building collective intelligence.

- [@YannicKilcher](https://www.youtube.com/@YannicKilcher) `[Individual]`, Yannic Kilcher: make videos about machine learning research papers, programming, and issues of the AI community, and the broader impact of AI in society.

- [@sentdex](https://www.youtube.com/@sentdex) `[Individual]`, sentdex: provides Python programming tutorials in machine learning, finance, data analysis, robotics, web development, game development and more.

- [@bmvabritishmachinevisionas8529](https://www.youtube.com/@bmvabritishmachinevisionas8529) `[Conferences]`, BMVA: British Machine Vision Association.

- [@ComputerVisionFoundation](https://www.youtube.com/@ComputerVisionFoundation) `[Conferences]`, Computer Vision Foundation (CVF): co-sponsored conferences on computer vision (e.g. CVPR and ICCV). 

- [@cvprtum](https://www.youtube.com/@cvprtum) `[University]`, Computer Vision Group at Technical University of Munich.

- [@UCFCRCV](https://www.youtube.com/@UCFCRCV) `[University]`, Center for Research in Computer Vision at University of Central Florida.

- [@dynamicvisionandlearninggr1022](https://www.youtube.com/@dynamicvisionandlearninggr1022) `[University]`, Dynamic Vision and Learning research group channel! Technical University of Munich.

- [@TubingenML](https://www.youtube.com/@TubingenML) `[University]`, Machine Learning groups at the University of Tübingen.

- [@computervisiontalks4659](https://www.youtube.com/@computervisiontalks4659) `[Talks]`, Computer Vision Talks.

- [@freecodecamp](https://www.youtube.com/@freecodecamp) `[Talks]`, Videos to learn how to code.

- [@LondonMachineLearningMeetup](https://www.youtube.com/@LondonMachineLearningMeetup) `[Talks]`, Largest machine learning community in Europe.

- [@LesHouches-iu6nv](https://www.youtube.com/@LesHouches-iu6nv) `[Talks]`, Summer school on Statistical Physics of Machine learning held in Les Houches, July 4 - 29, 2022.

- [@MachineLearningStreetTalk](https://www.youtube.com/@MachineLearningStreetTalk) `[Talks]`, top AI podcast on Spotify.

- [@WeightsBiases](https://www.youtube.com/@WeightsBiases) `[Talks]`, Weights and Biases team's conversations with industry experts, and researchers.

- [@PreserveKnowledge](https://www.youtube.com/@PreserveKnowledge/) `[Talks]`, Canada higher education media organization that focuses on advances in mathematics, computer science, and artificial intelligence. 

- [@TwoMinutePapers](https://www.youtube.com/@TwoMinutePapers) `[Papers]`, Two Minute Papers: Explaining AI papers in few mins.

- [@TheAIEpiphany](https://www.youtube.com/@TheAIEpiphany) `[Papers]`, Aleksa Gordić: x-Google DeepMind, x-Microsoft engineer explaining AI papers.

- [@bycloudAI](https://www.youtube.com/@bycloudAI) `[Papers]`, bycloud: covers the latest AI tech/research papers for fun.

- Unorganized/Unsorted:

  - https://www.youtube.com/@AAmini

  - https://www.youtube.com/@WhatsAI  

  - https://www.youtube.com/@mrdbourke

  - https://www.youtube.com/@marksaroufim

  - https://www.youtube.com/@NicholasRenotte

  - https://www.youtube.com/@abhishekkrthakur

  - https://www.youtube.com/@AladdinPersson

  - https://www.youtube.com/@CodeEmporium

  - https://www.youtube.com/@arp_ai

  - https://www.youtube.com/@CodeThisCodeThat

  - https://www.youtube.com/@connorshorten6311

  - https://www.youtube.com/@SmithaKolan

  - https://www.youtube.com/@AICoffeeBreak

  - https://www.youtube.com/@independentcode

  - https://www.youtube.com/@alfcnz

  - https://www.youtube.com/@KapilSachdeva

  - https://www.youtube.com/@AICoding

  - https://www.youtube.com/@mildlyoverfitted

---

## Mailing Lists

- [Vision Science](http://visionscience.com/mailman/listinfo/visionlist_visionscience.com), announcements about industry/academic jobs in computer vision around the world (in English).

- [bull-i3](https://listes.irit.fr/sympa/info/bull-i3), posts about job opportunities in computer vision in France (in French).

---

## Thanks

- [Frida de Sigley](https://github.com/fdsig) 

- Dan Harvey

- [CORE Conference Ranking](http://portal.core.edu.au/conf-ranks/?search=4603&by=all&source=CORE2021&sort=arank&page=1)

- [Scimago Journal Ranking](https://www.scimagojr.com/journalrank.php)

- [benthecoder/yt-channels-DS-AI-ML-CS](https://github.com/benthecoder/yt-channels-DS-AI-ML-CS)